• Laser & Optoelectronics Progress
  • Vol. 57, Issue 20, 201021 (2020)
Hong Zhang1、2, Yunyang Yan1、2、*, Yian Liu1, and Shangbing Gao2
Author Affiliations
  • 1School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu 223003, China;
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    DOI: 10.3788/LOP57.201021 Cite this Article Set citation alerts
    Hong Zhang, Yunyang Yan, Yian Liu, Shangbing Gao. Fire Detection Method Based on Localization Confidence and Region-Based Fully Convolutional Network[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201021 Copy Citation Text show less
    Detection diagram of R-FCN
    Fig. 1. Detection diagram of R-FCN
    Separable convolution
    Fig. 2. Separable convolution
    Incomplete information marked by anchors
    Fig. 3. Incomplete information marked by anchors
    Change of translation and scaling
    Fig. 4. Change of translation and scaling
    Comparison of iterations accuracy changes[11]. (a)FPN iterations accuracy changes; (b)R-CNN iterations accuracy changes
    Fig. 5. Comparison of iterations accuracy changes[11]. (a)FPN iterations accuracy changes; (b)R-CNN iterations accuracy changes
    Comparison of pooling. (a) ROI pooling; (b) precise ROI pooling
    Fig. 6. Comparison of pooling. (a) ROI pooling; (b) precise ROI pooling
    Non-maximum suppression
    Fig. 7. Non-maximum suppression
    Lack of localization confidence of NMS
    Fig. 8. Lack of localization confidence of NMS
    Prediction of IOU
    Fig. 9. Prediction of IOU
    Flow chart of LOF-FCN
    Fig. 10. Flow chart of LOF-FCN
    Comparison of fire missed detection rate
    Fig. 11. Comparison of fire missed detection rate
    Comparison of fire detection accuracy
    Fig. 12. Comparison of fire detection accuracy
    Experimental video. (a) Video 1; (b) video 2; (c) video 3; (d) video 4; (e) video 5; (f) video 6
    Fig. 13. Experimental video. (a) Video 1; (b) video 2; (c) video 3; (d) video 4; (e) video 5; (f) video 6
    MethodKernelSpeed /(frame/s)Accuracy /%
    R-FCN13×131596.9
    Separableconvolution13×1,1×132296.4
    Table 1. Comparison of the speed of the detection
    CategoryPictureNumber
    L-Fire12232104
    S-Fire35125921
    Total47358025
    Table 2. Training data
    ModelYOLOv3Fast-RCNNFaster-RCNNR-FCNLOF-FCN
    Speed /(frame/s)340.671317
    mAP /%76.254.083.980.381.7
    Table 3. Comparison of algorithm performance
    VideoVideo frameFire frameMethod in Ref. [6]Method in Ref. [8]Method in Ref. [9]Proposed method
    TP /%FP /%TP /%FP /%TP /%FP /%TP /%FP /%
    117015696.23.897.62.498.61.499.20.8
    223425794.55.593.26.892.77.395.44.6
    320018299.30.799.80.210001000
    41249399.70.31000100099.01.0
    Average182.0172.097.42.697.72.397.82.298.41.6
    Table 4. Detection results of flame
    VideoVideo frameFire frameMethod in Ref. [6]Method in Ref. [8]Method in Ref. [9]Proposed method
    TN /%FN /%TN /%FN /%TN /%FN /%TN /%FN /%
    5420098.81.297.32.795.44.698.31.7
    6376097.22.896.23.896.73.397.62.4
    Average398.00.098.02.096.83.296.13.998.02.0
    Table 5. Detection results of non-flame
    Hong Zhang, Yunyang Yan, Yian Liu, Shangbing Gao. Fire Detection Method Based on Localization Confidence and Region-Based Fully Convolutional Network[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201021
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